Modelling long-term heart rate variability: an ARFIMA approach

• Type: Article in International Scientific Journal
• Date: 2006

Authors

Argentina S Leite (Author) Other; Ana Paula Rocha (Author) FCUP; Eduarda E Silva (Author) FCUP; Ovidio Costa (Author) Other

Journal

Title: Biomedical Engineering-Biomedizinische Technik

Vol. 51

Pages: 215-219

ISSN: 0013-5585

Publisher: Walter De Gruyter

Indexing

ISI Web of Knowledge - 5 Citations

Scopus - 8 Citations

Scientific classification

FOS: Engineering and technology > Environmental biotechnology

Other information

Authenticus ID: P-004-P8F

DOI: 10.1515/bmt.2006.040

Abstract (EN): Long-term heart rate variability (HRV) series can be described by time-variant autoregressive modelling. HRV recordings show dependence between distant observations that is not negligible, suggesting the existence of long-range correlations. In this work, selective adaptive segmentation combined with fractionally integrated autoregressive moving-average models is used to capture long memory in HRV recordings. This approach leads to an improved description of the low- and high-frequency components in HRV spectral analysis. Moreover, it is found that in the 24-h recording of a case report, the long-memory parameter presents a circadian variation, with different regimes for day and night periods.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 5